Learning is a popular way in generating a global descriptor. PointNetVLAD[1]} extracts global descriptors from a given 3D point cloud through end-to-end training and shows high accuracy and robustness. LPD-Net[2]} adaptively selects \(K\)  values to extract a global descriptor with recognition and generalization when constructing a neighborhood map using \(K\)  Nearest Neighbors (KNN). SeqLPD[3]} is a neural network to extract global descriptors, where sequence matching reduces the computational time by selecting several super keyframes. GOSMatch[4]} is generated from the spatial relationship between semantics, and is applied to frame description and data association. However, a limitation of the learning-based approach is that they sometimes show poor generalization[5]}. In addition, these learning-based methods need a lot of training data. So, in this paper, we focus on a hand-craft global descriptor.
